Print quality diagnosis

ABSTRACT

In some examples, print quality diagnosis may include aligning corresponding characters between a scanned image of a printed physical medium aligned to a master image associated with generation of the printed physical medium to generate a common mask. Print quality diagnosis may further include determining, for each character of the scanned image, an average value associated with pixels within the common mask, and determining, for each corresponding character of the master image, the average value associated with pixels within the common mask. Further, print quality diagnosis may include determining, for each character of the common mask, a metric between the average values associated with the corresponding characters in the scanned and master images.

BACKGROUND

A printing device, such as a printer, multifunction printer, and/orother such devices may be described as a peripheral which is used tomake a persistent human readable representation of graphics or text onphysical media such as paper. A printing device may include variouscomponents to move the physical media from a first location, such as aninput tray, to a second location, such as an output tray.

BRIEF DESCRIPTION OF DRAWINGS

Features of the present disclosure are illustrated by way of example andnot limited in the following figure(s), in which like numerals indicatelike elements, in which:

FIG. 1 illustrates a layout of a print quality diagnosis apparatus,according to an example of the present disclosure;

FIG. 2 illustrates a process flow for the print quality diagnosisapparatus of FIG. 1, according to an example of the present disclosure;

FIG. 3 illustrates an image registration flow for the print qualitydiagnosis apparatus of FIG. 1, according to an example of the presentdisclosure;

FIG. 4 illustrates Harris Corners for image registration for the printquality diagnosis apparatus of FIG. 1, according to an example of thepresent disclosure;

FIG. 5 illustrates a scanned image and a master image overlapping witheach other for illustrating image registration for the print qualitydiagnosis apparatus of FIG. 1, according to an example of the presentdisclosure;

FIG. 6 illustrates matched feature pairs for the scanned image and themaster image of FIG. 5, according to an example of the presentdisclosure;

FIG. 7 illustrates the scanned image overlapped with the master image ofFIG. 5 after alignment for illustrating image registration for the printquality diagnosis apparatus of FIG. 1, according to an example of thepresent disclosure;

FIG. 8 illustrates a local alignment flow for text fading detection forthe print quality diagnosis apparatus of FIG. 1, according to an exampleof the present disclosure;

FIG. 9A illustrates text before alignment, and FIG. 9B illustrates textafter alignment for the text fading detection of FIG. 8 for the printquality diagnosis apparatus of FIG. 1, according to an example of thepresent disclosure;

FIG. 10A illustrates a scanned image without fading, and FIG. 10Billustrates a scanned image with fading for illustrating operation ofthe print quality diagnosis apparatus of FIG. 1, according to an exampleof the present disclosure;

FIG. 11A illustrates a histogram for the non-faded scanned image of FIG.10A, and FIG. 11B illustrates a histogram for the faded scanned image ofFIG. 10B for illustrating operation of the print quality diagnosisapparatus of FIG. 1, according to an example of the present disclosure;

FIGS. 12A and 12B, 13A and 13B, 14A and 14B, and 15A and 15B,respectively illustrate a scanned image and an associated histogram forillustrating operation of the print quality diagnosis apparatus of FIG.1, according to an example of the present disclosure;

FIG. 16 illustrates a block diagram for print quality diagnosis,according to an example of the present disclosure;

FIG. 17 illustrates a flowchart of a method for print quality diagnosis,according to an example of the present disclosure; and

FIG. 18 illustrates a further block diagram for print quality diagnosis,according to an example of the present disclosure.

DETAILED DESCRIPTION

For simplicity and illustrative purposes, the present disclosure isdescribed by referring mainly to examples. In the following description,numerous specific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be readily apparenthowever, that the present disclosure may be practiced without limitationto these specific details. In other instances, some methods andstructures have not been described in detail so as not to unnecessarilyobscure the present disclosure.

Throughout the present disclosure, the terms “a” and “an” are intendedto denote at least one of a particular element. As used herein, the term“includes” means includes but not limited to, the term “including” meansincluding but not limited to. The term “based on” means based at leastin part on.

A print quality diagnosis apparatus, a method for print qualitydiagnosis, and a non-transitory computer readable medium having storedthereon machine readable instructions to provide print quality diagnosisare disclosed herein. The apparatus, method, and non-transitory computerreadable medium disclosed herein provide for diagnosis of a printingdevice based on a built-in scan bar, for example, at an output end ofthe printing device. Thus, physical media that is printed may be scannedby the scan bar to identify print defects such as banding, streaking,text fading, etc. The print defects may be identified by comparing thescanned physical medium to a master image. In this regard, an image ofthe scanned physical medium (also denoted “scanned image”) may beinitially aligned with an associated master image by implementing afeature based image registration technique. Further, text regions of thetwo aligned images may be extracted and compared to identify any printdefects. For example, print defects may be identified by detecting textfading. In this regard, a text fading analysis technique may compare thecolor difference between two images, and the mean value of the colordifference distribution may be used as an indicator of the degree of thetext fading (where the text fading level is positively correlated withthe mean value). In response to a determination that the print qualityhas dropped below a specified print quality threshold (i.e., based onthe identification of print defects), a service call may be scheduledfor the printing device.

For the apparatus, method, and non-transitory computer readable mediumdisclosed herein, a text fading level may be used to determine an amountof toner remaining for each toner cartridge of the printing device(e.g., each cyan (C), magenta (M), yellow (Y), and key(K) tonercartridge). Other types of defects that may be detected in the textregions may include dark streaking, developer defects, banding, etc.

For the apparatus, method, and non-transitory computer readable mediumdisclosed herein, modules, as described herein, may be any combinationof hardware and programming to implement the functionalities of therespective modules. In some examples described herein, the combinationsof hardware and programming may be implemented in a number of differentways. For example, the programming for the modules may be processorexecutable instructions stored on a non-transitory machine-readablestorage medium and the hardware for the modules may include a processingresource to execute those instructions. In these examples, a computingdevice implementing such modules may include the machine-readablestorage medium storing the instructions and the processing resource toexecute the instructions, or the machine-readable storage medium may beseparately stored and accessible by the computing device and theprocessing resource. In some examples, some modules may be implementedin circuitry, such as one or more application specific integratedcircuits (ASICs).

FIG. 1 illustrates a layout of a print quality diagnosis apparatus(hereinafter also referred to as “apparatus 100”), according to anexample of the present disclosure.

Referring to FIG. 1, the apparatus 100 may include a scan trigger module102 to forward a notice to a port of a printing device 104 specifying alength of a print job that is received. A copy of an image that is to beprinted may be saved and denoted a master image 106. The master image106 may be used for comparison and analysis of a scanned image 108(i.e., a scanned image of a printed physical medium 110 based on theprint job).

The scan trigger module 102 may generate a trigger, for example, uponinitiation of the print job, and when the trigger is received by ascanning module 112, the scanning module 112 may initialize a scanner114 to scan the printed physical medium 110 to generate the scannedimage 108.

The apparatus 100 may include an image registration module 116 to alignthe scanned image 108 of the printed physical medium 110 to the masterimage 106 associated with generation of the printed physical medium 110.

The apparatus 100 may include a text fading detection module 118 toalign corresponding characters between the aligned scanned and masterimages to generate a common mask 120. The text fading detection module118 may determine, for each character of the scanned image 108, anaverage value (e.g., average L*a*b* as disclosed herein) associated withpixels within the common mask 120, and determine, for each correspondingcharacter of the master image 106, the average value associated withpixels within the common mask 120. The text fading detection module 118may determine, for each character of the common mask 120, a metric(e.g., an Euclidean distance as disclosed herein) between the averagevalues associated with the corresponding characters in the scanned andmaster images.

The apparatus 100 may include a print quality analysis module 122 toanalyze a histogram of determined metrics for characters of the commonmask to diagnose print quality of the printed physical medium 110.

In some examples, the apparatus 100 may include or be provided as acomponent of a printing device. Alternatively, the apparatus 100 may beprovided separately from the printing device. For example, the apparatus100 may be provided as a component of a print server to control aprinting operation of the printing device.

As will be appreciated, some examples of the apparatus 100 may beconfigured with more or fewer modules, where modules may be configuredto perform more or fewer operations. Furthermore, in some examples, themodules may be implemented by execution of instructions with aprocessing resource to cause the processing resource to perform thecorresponding operations.

FIG. 2 illustrates a process flow for the apparatus 100, according to anexample of the present disclosure.

Referring to FIG. 2, when the physical medium is to be printed, the scantrigger module 102 may forward a notice to the printing device 104specifying a length of the print job. For example, the scan triggermodule 102 may forward a notice to a port on the Internet Protocol (IP)address of the printing device 104 specifying a length of the print job.A copy of the image that is to be printed may also be saved based on theprint job size, and denoted the master image 106. The master image 106may be used for comparison and analysis of the scanned image 108 (i.e.,the scanned image of the printed physical medium 110). A trigger asshown in FIG. 2 may be generated by the scan trigger module 102, forexample, upon initiation of the print job. At block 200, when thetrigger is received by the scanning module 112, at block 202 thescanning module 112 may initialize the scanner 114. Further, thescanning module 112 may wait for a “top of physical medium” trigger tobegin scanning of the printed physical medium 110. At block 204, thescanned image 108 and the master image 106 may be processed by the imageregistration module 116 and the text fading detection module 118. Theoutput of the image processing at block 204 may include a “pass” or“fail”, or another indication. For example, a “pass” may be output ifthe results of the processing by the image registration module 116 andthe text fading detection module 118 meet or exceed a print qualitythreshold, and otherwise, a “fail” may be output. For example, if thetext fading detected by the text fading detection module 118 is below aspecified text fading based threshold, a “pass” may be output. Aclassifier may be included at the output of block 204, where an outputof the classifier may include a rank, or a pass/fail as shown in FIG. 2.

With reference to block 204, in the event that image processing takeslonger than the physical medium throughput for multipage print jobs,such that as a current physical medium is being processed, a newphysical medium arrives before the current physical medium is processed,the new physical medium (and any further physical media) may be omittedfrom processing or otherwise processed even after the print job iscompleted. Thus, scanned images for further physical media may be savedand processed by the image registration module 116 and the text fadingdetection module 118 during printing device downtime.

FIG. 3 illustrates an image registration flow for the apparatus 100,according to an example of the present disclosure.

With respect to image registration, the scanned image 108 may be subjectto translation, scaling, skewing, etc. Accordingly, in order to comparethe scanned image 108 with the master image 106, the scanned image 108and the master image 106 may be spatially aligned. In this regard, theimage registration module 116 may perform a global image registration ofthe scanned image 108 with the master image 106. The image registrationmodule 116 may perform a feature based image registration by identifyingfeature points, for example corners, of objects in the scanned image 108and the master image 106, and determining a best match between thefeatures. The features may be used to establish point-to-pointcorrespondence, and to estimate geometric transformation. The geometrictransformation may be applied to the target image (e.g., the scannedimage 108 or the master image 106) to spatially align with the sourceimage (e.g., the other one of the scanned image 108 or the master image106).

Referring to FIG. 3, with respect to image registration, at block 300,the image registration module 116 may convert the scanned image 108(which may also be denoted “test image”, and converted, if needed, to ared, green and blue (RGB) format) and the master image 106 to grayscale.For example, for grayscale, the L* component of the L*a*b* value of eachpixel may be used.

At block 302, the image registration module 116 may downsample thescanned image 108 and the master image 106 to a lower resolution forfaster processing. For example, downsampling may be performed by blockaveraging pixels, and retaining an average for each block of pixels.

At block 304, the image registration module 116 may perform histogrammatching on the lower resolution scanned image 108 and the master image106 to match the scanned image 108 to the master image 106. For example,histogram matching may be based on the cumulative distribution function,which may be described as the indefinite sum of the histogram. Forexample, histogram matching may include the matching of the histogram ofone image to the histogram of another image. The matching may beperformed by using the cumulative distribution function (CDF) of twoimages as follows. First, the CDF of a reference image may be determined(e.g., red curve (CDF1), the CDF of the image to be matched: blue curve(CDF2)). For each pixel value, x1 from 0 to 255 in CDF1, determine x2 inCDF2 so that it yields the same CDF. If this operation is performed forall points from 0 to 255, this operation will generate a look up table(LUT). The LUT may be applied to the image to be matched, that is if apixel value x2 is read from the image to be matched, this pixel value ismodified to x1. These operations may be performed once for a gray image,and for a color image, the same operations may be applied for each layerseparately. After histogram matching, the colors of the image to bematched appear to be more similar to the reference image. Accordingly,since feature matching at block 310 (discussed below) may be based oncomparing the pixel intensity, histogram matching at block 304facilitates feature matching.

At block 306, the image registration module 116 may extract featurepoints from the low-resolution grayscale images, which have beenmatched. With respect to feature point extraction, according to anexample, the Harris Corner technique may be implemented to detectfeature points.

FIG. 4 illustrates Harris Corners for image registration for theapparatus 100, according to an example of the present disclosure.Referring to FIG. 4, for Harris Corners, I represents an input image,I_(x) and I_(y) represent the partial derivatives with respect to x andy, G represents a Gaussian filter, K represents a sensitivity factor,and CSF represents a corner strength function. In this regard, CSF(x, y)will be relatively large when there is a strong gradient along both thex direction and the y direction at point (x, y), and CSF(x, y) will beapproximately equal to zero at a smooth area. Points whose cornerstrength is larger than a threshold may be selected as feature interestpoints.

With the feature points being extracted, at block 308 the imageregistration module 116 may extract feature descriptors at each featurepoint. Assuming that the scanned image 108 is skewed by a relativelysmall angle with respect to the master image 106, a block of pixelscentered at each feature point may be extracted as the featuredescriptors. For example, for each Harris Corner, a feature descriptormay represent a 10×10 block of pixels centered at that Harris Corner, asdisclosed herein with reference to FIG. 4. Feature descriptors mayinclude non-free feature descriptors such as scale-invariant featuretransform (SIFT) and speeded up robust features (SURF), free featuredescriptors such as binary robust independent elementary features(BRIEF), binary robust invariant scalable keypoints (BRISK), and fastretina keypoint (FREAK), and the block feature descriptor. For the“block” feature descriptor, all image points within a window centered ata key point may be extracted.

At block 310, the image registration module 116 may match the extractedfeature descriptors, for example, by using Squared Sum of intensityDifferences (SSD). For the SSD, for each descriptor in f1, the SSDrelative to all the descriptors in f2 may be determined, and the minimumSSD descriptor in f2 may be identified, which is considered as a matchto the descriptor in f1. For the SSD, f1 may represent the featurevector (array of features found) in the scanned image 108. Further, f2may represent the feature vector found in the master image 106. However,the scanned image 108, which may be distorted with text fading, may havea different intensity with respect to the master image 106. In thisregard, matching accuracy may be improved if the histogram of thescanned image 108 is matched with the master image 106 (e.g., at block304) before the feature matching at block 310.

With respect to matching of the extracted feature descriptors, in theevent that the scanned image 108 and the master image 106 include text(and no other graphics, etc.), a text character that is on the top of ascanned image 108 may be matched with the same text character thatappears in the middle or bottom of the master image 106. In this regard,the spatial distance between the feature pairs being matched may belimited to eliminate such incorrect matching.

At block 312, the image registration module 116 may determine ageometric transformation matrix, such as affine, from the matchedfeature pairs. According to an example, the image registration module116 may utilize techniques such as random sample consensus (RANSAC),maximum likelihood sample consensus (MLESAC), etc., to determine thegeometric transformation matrix.

At block 314, the image registration module 116 may apply thetransformation matrix to the full resolution scanned image 108 (insteadof the low resolution scanned image 108) to generate a registered image.In this regard, the translation parameters in the transformation matrixmay be scaled up by the downsampling rate before being applied. Theregistered image may include the scanned image 108 registered to themaster image 106.

FIG. 5 illustrates a scanned image and a master image overlapping witheach other for illustrating image registration for the apparatus 100,according to an example of the present disclosure.

For the example of FIG. 5, the scanned image 108 and the master image106 may include a resolution of approximately 3200×2464 at a spatialresolution of 300 dpi. As shown in FIG. 5, the scanned image 108 and themaster image 106 are misaligned both vertically and horizontally in theorientation of FIG. 5. Both the scanned image 108 and the master image106 may be converted to grayscale and then downsampled by four in bothvertical and horizontal directions in the orientation of FIG. 5. Thisresults in two 800×600 low resolution images as shown side by side inFIG. 6, where FIG. 6 illustrates matched feature pairs for the scannedimage and the master image of FIG. 5, according to an example of thepresent disclosure.

For FIG. 6, the circles represent the feature points, and the featurepairs connected by straight lines are matched. As shown in FIG. 6, thereare several mismatched pairs in the top half of the images at 600. Evenwith some mismatched feature pairs, these outliers may be rejected, forexample, by the RANSAC or MLESAC techniques.

FIG. 7 illustrates the scanned image overlapped with the master image ofFIG. 5 after alignment for illustrating image registration for theapparatus 100, according to an example of the present disclosure.

Referring to FIG. 7, even though the results of image registration forthe scanned image 108 and the master image 106 appear to be acceptable,at 700, it can be seen that the overlapped images still includemisalignment. In order to reduce this misalignment, the text fadingdetection module 118 may perform a localized text alignment.

With respect to text fading detection, one technique of text fadingdetection includes comparing a text faded physical medium with the paperwhite for each text character by determining a mean ΔE in L*a*b* colorspace. L*a*b* may represent an International Commission on Illumination(CIE) uniform color space, where L* represents lightness, a* representsred-green, and b* represents yellow-blue. This system is based on the anopponent color model for the human visual system. ΔE (or ΔE*ab) is theEuclidean distance between two points (two pixels) in the CIEL*a*b*color space, which is a perceptual uniform color space. ΔEreflects the perceptual difference of two colors as follows:ΔE _(ab)*=√{square root over ((L ₂ *−L ₁*)²+(a ₂*−a₁*)²+(b ₂*−b₁*)²)}The above equation may represent a pixel-wise operation. A textcharacter may include a plurality of pixels. Accordingly, the mean ofthe ΔE may be used to represent the average perceptual differencebetween two text characters. The histogram of all the characters of theentire physical medium will appear diverged if some of the textcharacters are faded. If there is no fading, or all of the charactersare faded, then the histogram will appear more concentrated.

With respect to text fading detection, if a physical medium includestext characters with different colors, then the mean ΔE for differentcolor text characters may vary, and the histogram may become diverged.To address this aspect, because of the availability of the master image106, the text fading detection module 118 may directly determine themean ΔE between the scanned image 108 and the master image 106 for eachcharacter. For this comparison, the master image 106 may be firstconverted to scanner RGB.

If a physical medium includes a relatively small amount of fading, thenthe faded characters may not affect the overall histogram since theyaccount for a relatively small population, but these faded charactersmay still be noticeable to human eyes. To address this aspect, the textfading detection module 118 may divide the physical medium into severalblocks or strips, and locally evaluate the histogram for each block orstrip instead of the entire physical medium. Therefore, when even asmall number of text characters include fading, the local regionhistogram would still diverge.

With respect to local alignment for text fading detection, the textfading detection module 118 may determine the color difference for allof the text character pixels. In this regard, FIG. 8 illustrates a localalignment flow for text fading detection for the apparatus 100,according to an example of the present disclosure.

Referring to FIG. 8, at block 800, the text fading detection module 118may perform adaptive thresholding to binarize a gray image, separatingtext characters from the background. With respect to adaptivethresholding, the text fading detection module 118 may subtract from thepixel value the median value of the pixels in a local block, and comparethe difference with a threshold. This comparison may establish theregion of pixels that correspond to the printed text character. Theoutput of FIG. 8 may represent a pixel shift that may be applied to thescanned image 108 to align each individual character in preparation forfurther analysis.

At block 802, the text fading detection module 118 may connect thepixels in the text characters by performing a connected componentprocess. The connected components process may represent a technique forgrouping pixels according to their association with a single textcharacter. The connected components process may be used to extractinformation from each text character, such as size, height, width, etc.In this regard, the text fading detection module 118 may identifycharacters (i.e., letters) in both the master image 106 and the scannedimage 108 via connected components.

At block 804, the text fading detection module 118 may remove noise fromthe registered image, for example, by performing a morphologicaloperation on the registered image. The morphological operation may bedescribed as a closing which fills in small isolated regions that shouldhave been included in the character mask, but which failed to exceed theaforementioned threshold, and remove very small character regions thatare due to noise. The text characters may be enlarged or reduced usingmorphological operations. Morphological operations may either add anadditional layer of pixels to the outside or inside of the characters,or remove a layer of pixels around the characters. This serves thepurpose to remove holes or gaps in a character, or removing dots thatare not associated with a character.

At block 806, the text fading detection module 118 may extract textcharacter components from the master image 106. The text charactercomponents may be identified by a global thresholding operation followedby application of the connected components process to group pixelscorresponding to each separate character type. Further, these operationsmay be followed by a local thresholding operation within a bounding boxincluding each connected component to further define boundaries of thatconnected component. The text character component may represent a listof the pixels corresponding to that particular text character. The textcharacter components may include information such as area, height,width, perimeter length, pixel values, etc., associated with textcharacters.

For each of the text character components extracted at block 806, atblock 808, the text fading detection module 118 may use the extractedtext character component as a template to determine a match in thescanned image 108 (i.e., the scanned binary image) inside a localizedrange. Since a given character, such as the letter “e”, for example, mayoccur at many locations within the scanned image 108, the geographicalregion of potential matches may be limited. In this manner, a letter “e”in the scanned image 108 is not matched with a different letter “e” inthe master image 106. In this regard, the text fading detection module118 may align the characters in the scanned image 108 to the charactersin the master image 106. The searched range may depend on the accuracyof registration of the scanned image 108 and the master image 106. Thesearch range may be determined by a training process using arepresentative set of master-target pairs of pages. The performance withdifferent search ranges may be evaluated, and the search range thatyields the best overall performance may be selected. Thus, with respectto block 808, the components extracted from each character may be usedto determine if the characters match and are indeed the same characters.Each character in the scanned image 108 may be compared to a region inthe master image 106. The region may be specified around the matchingcharacter with a window that does not fully include characters near thatcharacter. The two regions may be compared using a template matchingtechnique as discussed above. The template matching technique maycompare the regions with a sliding window, and the best overlappingwindow may be used for the text alignment in FIG. 9B.

FIG. 9A illustrates text before alignment, and FIG. 9B illustrates textafter alignment for the text fading detection of FIG. 8 for theapparatus 100, according to an example of the present disclosure.

Referring to FIG. 9B, with respect to local alignment performed by thetext fading detection module 118, each character of the master image 106is shown as being matched with a text character in the scanned image108. At the completion of the local alignment process, a list ofcomponents of text characters may be generated, and each component mayinclude a pixel list that may be used for pixel-wise color comparison bythe text fading detection module 118. A component may refer to all ofthe pixels associated with a particular text character, as determined bythe connected components process. With respect to referencing of thesepixels, the pixels may be arranged in a list. Each row in such a listmay include the row and column coordinates of the pixel. Each row mayalso include the value of the pixel at the row and column coordinates,expressed, for example, in terms of R, G, and B, or L*, a*, and b*.

With respect to color comparison, as discussed above, the text fadingdetection module 118 may utilize the metric ΔE, which represents theEuclidean distance between two points in L* b* color space. The pixellist extracted previously, which may be in scanner calibrated RGB, mayneed to be converted to L*a*b* first, and then ΔE may be determined foreach pixel in the list between the master image 106 and the scannedimage 108. The mean ΔE for each pair of components of text charactersmay indicate their perceptual difference on average. When a textcharacter is faded, regardless of its color, the mean ΔE will be becomerelatively large.

With respect to mean ΔE determination, according to an example,(R_(i),G_(i),B_(i)) may represent the i_(th) pixel triple in C_(k)(pixel list of the K_(th) component in the master image 106, where i=1 .. . N, N is the number of pixel triples in C_(k), k=1 . . . M, and, M isthe number of components in the entire master image 106 (or a strip).Each (R_(i),G_(i),B_(i)) and (R′_(i),G′_(i),B′_(i)) triple may beconverted to L*a*b* color space, which results in (L*_(i),a*_(i),b*_(i)) and (L′*_(i), a′*_(i), b′*_(i)). (R′_(i),G′_(i),B′_(i)) mayrepresent the i_(th) pixel triple in C′_(k) (pixel list of the componentin the scanned image 108 that is template matched with the K_(th)component in the master image 106). The color difference between i_(th)triple pair may be represented as:ΔE _(ab)*=√{square root over ((L ₂ *−L ₁*)²+(a ₂*−a₁*)²+(b ₂*−b₁*)²)}The mean ΔE of all the N pixels in C_(k) may be represented as:

${u\;\Delta\; E_{ab}^{*}} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{\Delta\; E_{ab}^{*}i}}}$The mean ΔE may represent, on average, how one component (or textcharacter) in the master image 106 is perceptually different from thetemplate matched component in the scanned image 108.

FIG. 10A illustrates a scanned image without fading, and FIG. 10Billustrates a scanned image with fading for illustrating operation ofthe apparatus 100, according to an example of the present disclosure.Further, FIG. 11A illustrates a histogram for the non-faded scannedimage of FIG. 10A, and FIG. 11B illustrates a histogram for the fadedscanned image of FIG. 10B for illustrating operation of the apparatus100, according to an example of the present disclosure.

Referring to FIGS. 10A and 11A, if the histogram of the mean ΔE of allthe characters is plotted, an unfaded physical medium such as thephysical medium shown in FIG. 10A will exhibit a relatively narrowspread histogram as shown in FIG. 11A. In contrast, when fading exists,such as the sample of FIG. 10B, the histogram will spread out as shownin FIG. 11B, and further, the mean will shift to the right (i.e.,include a larger value). However, the histogram may become lessdispersed as well when the entire physical medium is faded. Therefore,the mean of the mean ΔE may be used as a measure of the fading level.For example, for a relatively narrow strip of text characters that arefaded, the mean of the mean ΔE in a large degree may be dependent on thelarger population of non-faded text characters. The fading may be morereadily detected if a physical medium is divided into several smallregions, for example strips, and then the mean of the mean ΔE isanalyzed locally for each region.

FIGS. 12A and 12B, 13A and 13B, 14A and 14B, and 15A and 15B,respectively illustrate a scanned image and an associated histogram forillustrating operation of the apparatus 100, according to an example ofthe present disclosure.

Referring to FIGS. 12A, 13A, 14A, and 15A, a series of scanned imageswith increasing levels of text fading are shown. For visualizationpurpose, all of the scanned images are divided into four horizontalstrips. However, the scanned images may be divided into any number ofstrips (e.g., eight strips or more depending on the type of image beinganalyzed). The histogram plotted to the right of each scanned image(e.g., FIGS. 12B, 13B, 14B, and 15B) may represent the distribution ofthe mean ΔE for each character in each strip compared with a masterimage, which is not shown.

Referring to FIGS. 12A and 12B, the scanned image of FIG. 12A is anunfaded image (i.e., an image of an unfaded physical medium), andtherefore, the associated histograms of FIG. 12B for the four stripsshown in FIG. 12A are relatively narrow (i.e., not spread out) andinclude peaks that are located at their means. In this case, the meansare all less than a specified threshold, such as approximately 12, whichindicates that the print quality of the printed physical medium 110 hasnot reduced.

Referring to FIGS. 13A and 13B, the scanned image of FIG. 13A includesfading in the middle two strips, and thus the middle two histograms ofFIG. 13B begin to spread to the right (e.g., include a relatively widespread relative to a specified spread, such as the corresponding spreadof FIG. 12B) and their means become larger. In this case, the means ofthe middle two strips are greater than a specified threshold, such asapproximately 12, which indicates that the print quality of the printedphysical medium 110 is reduced. The reduction of the print quality ofthe printed physical medium 110 may be determined as a ratio (or anotherfactor) of a mean to a specified mean threshold. For the top and bottomstrips that include virtually no fading, their histograms remainrelatively unchanged.

Referring to FIGS. 14A and 14B, the scanned image of FIG. 14A includesincreased fading in the middle two strips (compared to FIG. 13A), andthus the middle two histograms of FIG. 14B begin to spread even furtherto the right and their means become even larger compared to FIG. 13B.For the top and bottom strips that include virtually no fading, theirhistograms remain relatively unchanged.

Referring to FIGS. 15A and 15B, the scanned image of FIG. 15A includesincreased fading in the middle two strips (compared to FIGS. 13A and14A) and some increase in fading in the top and bottom strips (comparedto FIGS. 13A and 14A). In this regard, the middle two histograms beginto spread even further to the right and their means become even largercompared to FIGS. 13B and 14B. For the top and bottom strips thatinclude increased fading, their histograms begin to spread to the rightand their means become larger compared to FIGS. 12B, 13B, and 14B.

FIGS. 16-18 respectively illustrate a block diagram 1600, a flowchart ofa method 1700, and a further block diagram 1800 for print qualitydiagnosis, according to examples. The block diagram 1600, the method1700, and the block diagram 1800 may be implemented on the apparatus 100described above with reference to FIG. 1 by way of example and notlimitation. The block diagram 1600, the method 1700, and the blockdiagram 1800 may be practiced in other apparatus. In addition to showingthe block diagram 1600, FIG. 16 shows hardware of the apparatus 100 thatmay execute the instructions of the block diagram 1600. The hardware mayinclude a processor 1602, and a memory 1604 storing machine readableinstructions that when executed by the processor cause the processor toperform the instructions of the block diagram 1600. The memory 1604 mayrepresent a non-transitory computer readable medium. FIG. 17 mayrepresent a method for print quality diagnosis, and the steps of themethod. FIG. 18 may represent a non-transitory computer readable medium1802 having stored thereon machine readable instructions to provideprint quality diagnosis. The machine readable instructions, whenexecuted, cause a processor 1804 to perform the instructions of theblock diagram 1800 also shown in FIG. 18.

The processor 1602 of FIG. 16 and/or the processor 1804 of FIG. 18 mayinclude a single or multiple processors or other hardware processingcircuit, to execute the methods, functions and other processes describedherein. These methods, functions and other processes may be embodied asmachine readable instructions stored on a computer readable medium,which may be non-transitory (e.g., the non-transitory computer readablemedium 1802 of FIG. 18), such as hardware storage devices (e.g., RAM(random access memory), ROM (read only memory), EPROM (erasable,programmable ROM), EEPROM (electrically erasable, programmable ROM),hard drives, and flash memory). The memory 1604 may include a RAM, wherethe machine readable instructions and data for a processor may resideduring runtime.

Referring to FIGS. 1-16, and particularly to the block diagram 1600shown in FIG. 16, at block 1606, the memory 1604 may includeinstructions to align (e.g., by the image registration module 116) ascanned image 108 of a printed physical medium 110 to a master image 106associated with generation of the printed physical medium 110.

At block 1608, the memory 1604 may include instructions to align (e.g.,by the text fading detection module 118) corresponding charactersbetween the aligned scanned and master images to generate a common mask120.

At block 1610, the memory 1604 may include instructions to determine(e.g., by the text fading detection module 118), for each character ofthe scanned image 108, an average value (e.g., average L*a*b*)associated with pixels within the common mask 120, and determine, foreach corresponding character of the master image 106, the average valueassociated with pixels within the common mask 120.

At block 1612, the memory 1604 may include instructions to determine(e.g., by the text fading detection module 118), for each character ofthe common mask 120, a metric (e.g., ΔE) between the average valuesassociated with the corresponding characters in the scanned and masterimages.

At block 1614, the memory 1604 may include instructions to analyze(e.g., by the print quality analysis module 122) a histogram (e.g., asdisclosed herein with reference to FIGS. 10A-15B) of determined metricsfor characters of the common mask 120 to diagnose print quality of theprinted physical medium 110.

According to an example, the machine readable instructions to align thescanned image 108 of the printed physical medium 110 to the master image106 associated with generation of the printed physical medium 110further comprise machine readable instructions to identify a pluralityof feature points for the scanned and master images, and extract, foreach of the plurality of feature points, feature descriptors. Further,the machine readable instructions may cause a processor to match theextracted feature descriptors that are disposed within a specifieddistance between the scanned and master images to generate matchedfeature pairs, generate a transformation matrix based on the matchedfeature pairs, and apply the transformation matrix to the scanned image108 to align the scanned image 108 of the printed physical medium 110 tothe master image 106 associated with generation of the printed physicalmedium 110 (e.g., see discussion with respect to FIG. 3).

According to an example, the machine readable instructions to aligncorresponding characters between the aligned scanned and master imagesto generate the common mask 120 further comprise machine readableinstructions to identify characters in the scanned and master images,match corresponding identified characters in the scanned and masterimages, and align corresponding matched characters between the alignedscanned and master images to generate the common mask 120 (e.g., seediscussion with respect to FIG. 8).

According to an example, the machine readable instructions to determine,for each character of the scanned image 108, the average valueassociated with pixels within the common mask 120, and determine, foreach corresponding character of the master image 106, the average valueassociated with pixels within the common mask 120 further comprisemachine readable instructions to determine, for each character of thescanned image 108, the average value that includes L*a*b* associatedwith pixels within the common mask 120, where L* represents lightness,a* represents red-green, and b* represents yellow-blue, and determine,for each corresponding character of the master image 106, the averagevalue that includes L*a*b* associated with pixels within the common mask120 (e.g., see discussion with respect to FIG. 8).

According to an example, the machine readable instructions to determine,for each character of the common mask 120, the metric between theaverage values associated with the corresponding characters in thescanned and master images further comprise machine readable instructionsto determine, for each character of the common mask 120, the metric thatincludes an Euclidean distance between the average values associatedwith the corresponding characters in the scanned and master images(e.g., see discussion with respect to FIG. 8).

According to an example, the machine readable instructions to analyzethe histogram of determined metrics for characters of the common mask120 to diagnose the print quality of the printed physical medium 110further comprise machine readable instructions to cause the processor todivide the printed physical medium 110 into a plurality of sections,generate, for each section of the plurality of sections, a histogram ofthe determined metrics, and analyze the histogram of the determinedmetrics for a section of the plurality of sections to diagnose the printquality of the section of the plurality of sections of the printedphysical medium 110 (e.g., see discussion with respect to FIGS. 8 and10A-15B).

According to an example, the machine readable instructions to analyzethe histogram of determined metrics for characters of the common mask120 to diagnose the print quality of the printed physical medium 110further comprise machine readable instructions to determine whether thehistogram of the determined metrics includes a relatively narrow spreador a relatively wide spread relative to a specified spread of thehistogram, and in response to a determination that the histogram of thedetermined metrics includes the relatively wide spread, determine areduction in the print quality of the printed physical medium 110 (e.g.,see discussion with respect to FIGS. 8 and 10A-15B).

According to an example, the machine readable instructions to analyzethe histogram of determined metrics for characters of the common mask120 to diagnose the print quality of the printed physical medium 110further comprise machine readable instructions to determine a meanassociated with each of the determined metrics (e.g., mean ΔE),determine whether a mean (e.g., mean of the mean ΔE) determined fromeach mean associated with each of the determined metrics is greater thana specified mean value threshold, and in response to a determinationthat the mean determined from each mean associated with each of thedetermined metrics is greater than the specified mean value threshold,determine a reduction in the print quality of the printed physicalmedium 110 (e.g., see discussion with respect to FIGS. 8 and 10A-15B).

Referring to FIGS. 1-3, and 17, and particularly FIG. 17, for the method1700, at block 1702, the method may include aligning (e.g., by the textfading detection module 118) corresponding characters between a scannedimage 108 of a printed physical medium 110 aligned to a master image 106associated with generation of the printed physical medium 110 togenerate a common mask 120.

At block 1704, the method may include determining (e.g., by the textfading detection module 118), for each character of the scanned image108, an average value associated with pixels within the common mask 120,and determining, for each corresponding character of the master image106, the average value associated with pixels within the common mask120.

At block 1706, the method may include determining (e.g., by the textfading detection module 118), for each character of the common mask 120,a metric between the average values associated with the correspondingcharacters in the scanned and master images.

At block 1708, the method may include dividing (e.g., by the text fadingdetection module 118) the printed physical medium 110 into a pluralityof sections.

At block 1710, the method may include generating (e.g., by the textfading detection module 118), for each section of the plurality ofsections, a histogram of the determined metrics.

At block 1712, the method may include analyzing (e.g., by the printquality analysis module 122) the histogram of the determined metrics fora section of the plurality of sections to diagnose print quality of thesection.

Referring to FIGS. 1-3, and 18, and particularly FIG. 18, for the blockdiagram 1800, at block 1806, the non-transitory computer readable medium1802 may include instructions to align (e.g., by the text fadingdetection module 118) corresponding characters between a scanned image108 of a printed physical medium 110 aligned to a master image 106associated with generation of the printed physical medium 110 togenerate a common mask 120.

At block 1808, the non-transitory computer readable medium 1802 mayinclude instructions to determine (e.g., by the text fading detectionmodule 118), for each character of the scanned image 108, an averagevalue associated with pixels within the common mask 120, and determine,for each corresponding character of the master image 106, the averagevalue associated with pixels within the common mask 120.

At block 1810, the non-transitory computer readable medium 1802 mayinclude instructions to determine (e.g., by the text fading detectionmodule 118), for each character of the common mask 120, a metric betweenthe average values associated with the corresponding characters in thescanned and master images.

At block 1812, the non-transitory computer readable medium 1802 mayinclude instructions to determine (e.g., by the text fading detectionmodule 118) a mean associated with each determined metric.

At block 1814, the non-transitory computer readable medium 1802 mayinclude instructions to determine (e.g., by the print quality analysismodule 122) whether a mean determined from each mean associated witheach determined metric is greater than a specified mean value threshold.

At block 1816, in response to a determination that the mean determinedfrom each mean associated with each determined metric is greater thanthe specified mean value threshold, the non-transitory computer readablemedium 1802 may include instructions to determine (e.g., by the printquality analysis module 122) a reduction in print quality of the printedphysical medium 110.

What has been described and illustrated herein is an example along withsome of its variations. The terms, descriptions and figures used hereinare set forth by way of illustration only and are not meant aslimitations. Many variations are possible within the spirit and scope ofthe subject matter, which is intended to be defined by the followingclaims—and their equivalents—in which all terms are meant in theirbroadest reasonable sense unless otherwise indicated.

What is claimed is:
 1. A print quality diagnosis apparatus comprising: aprocessor; and a memory storing machine readable instructions that whenexecuted by the processor cause the processor to: align a scanned imageof a printed physical medium to a master image associated withgeneration of the printed physical medium; align correspondingcharacters between the aligned scanned and master images to generate acommon mask; determine, for each character of the scanned image, anaverage value associated with pixels within the common mask, anddetermine, for each corresponding character of the master image, theaverage value associated with pixels within the common mask; determine,for each character of the common mask, a metric between the averagevalues associated with the corresponding characters in the scanned andmaster images, wherein the metric between the average values comprises aEuclidean distance between the average values associated with thecorresponding characters in the scanned and master images; and analyze ahistogram of determined metrics for characters of the common mask todiagnose print quality of the printed physical medium.
 2. The apparatusaccording to claim 1, wherein the machine readable instructions to alignthe scanned image of the printed physical medium to the master imageassociated with generation of the printed physical medium furthercomprise machine readable instructions to cause the processor to:identify a plurality of feature points for the scanned and masterimages; extract, for each of the plurality of feature points, featuredescriptors; match the extracted feature descriptors that are disposedwithin a specified distance between the scanned and master images togenerate matched feature pairs; generate a transformation matrix basedon the matched feature pairs; and apply the transformation matrix to thescanned image to align the scanned image of the printed physical mediumto the master image associated with generation of the printed physicalmedium.
 3. The apparatus according to claim 1, wherein the machinereadable instructions to align corresponding characters between thealigned scanned and master images to generate the common mask furthercomprise machine readable instructions to cause the processor to:identify characters in the scanned and master images; matchcorresponding identified characters in the scanned and master images;and align corresponding matched characters between the aligned scannedand master images to generate the common mask.
 4. The apparatusaccording to claim 1, wherein the machine readable instructions todetermine, for each character of the scanned image, the average valueassociated with pixels within the common mask, and determine, for eachcorresponding character of the master image, the average valueassociated with pixels within the common mask further comprise machinereadable instructions to cause the processor to: determine, for eachcharacter of the scanned image, the average value that includes L*a*b*associated with pixels within the common mask, where L* representslightness, a* represents red-green, and b* represents yellow-blue; anddetermine, for each corresponding character of the master image, theaverage value that includes L*a*b* associated with pixels within thecommon mask.
 5. The apparatus according to claim 1, wherein the machinereadable instructions to analyze the histogram of determined metrics forcharacters of the common mask to diagnose the print quality of theprinted physical medium further comprise machine readable instructionsto cause the processor to: divide the printed physical medium into aplurality of sections; generate, for each section of the plurality ofsections, a histogram of the determined metrics; and analyze thehistogram of the determined metrics for a section of the plurality ofsections to diagnose the print quality of the section of the pluralityof sections of the printed physical medium.
 6. The apparatus accordingto claim 1, wherein the machine readable instructions to analyze thehistogram of determined metrics for characters of the common mask todiagnose the print quality of the printed physical medium furthercomprise machine readable instructions to cause the processor to:determine whether the histogram of the determined metrics includes arelatively narrow spread or a relatively wide spread relative to aspecified spread of the histogram; and in response to a determinationthat the histogram of the determined metrics includes the relativelywide spread, determine a reduction in the print quality of the printedphysical medium.
 7. The apparatus according to claim 1, wherein themachine readable instructions to analyze the histogram of determinedmetrics for characters of the common mask to diagnose the print qualityof the printed physical medium further comprise machine readableinstructions to cause the processor to: determine a mean associated witheach of the determined metrics; determine whether a mean determined fromeach mean associated with each of the determined metrics is greater thana specified mean value threshold; and in response to a determinationthat the mean determined from each mean associated with each of thedetermined metrics is greater than the specified mean value threshold,determine a reduction in the print quality of the printed physicalmedium.
 8. A method for print quality diagnosis comprising: aligning, bya processor, corresponding characters between a scanned image of aprinted physical medium aligned to a master image, associated withgeneration of the printed physical medium to generate a common mask;determining, for each character of the scanned image, an average valueassociated with pixels within the common mask, and determining, for eachcorresponding character of the master image, the average valueassociated with pixels within the common mask; determining, for eachcharacter of the common mask, a metric between the average valuesassociated with the corresponding characters in the scanned and masterimages, wherein the metric between the average values comprises aEuclidean distance between the average values associated with thecorresponding characters in the scanned and master images; dividing theprinted physical medium into a plurality of sections; generating, foreach section of the plurality of sections, a histogram of the determinedmetrics; and analyzing the histogram of the determined metrics for asection of the plurality of sections to diagnose print quality of thesection.
 9. The method according to claim 8, wherein aligningcorresponding characters between the scanned image of the printedphysical medium aligned to the master image associated with generationof the printed physical medium to generate the common mask furthercomprises: identifying characters in the scanned and master images;matching corresponding identified characters in the scanned and masterimages; and aligning corresponding matched characters between thealigned scanned, and master images to generate the common mask.
 10. Themethod according to claim 8, wherein determining, for each character ofthe scanned image, the average value associated with pixels within thecommon mask, and determining, for each corresponding character of themaster image, the average value associated with pixels within the commonmask further comprises: determining, for each character of the scannedimage, the average value that includes L*a*b* associated with pixelswithin the common mask, where L* represents lightness, a* representsred-green, and b* represents yellow-blue; and determining, for eachcorresponding character of the master image, the average value thatincludes L*a*b* associated with pixels within the common mask.
 11. Anon-transitory computer readable medium having stored thereon machinereadable instructions to provide print quality diagnosis, the machinereadable instructions, when executed, cause a processor to: aligncorresponding characters between a scanned image of a printed physicalmedium aligned to a master image associated with generation of theprinted physical medium to generate a common mask; determine, for eachcharacter of the scanned image, an average value associated with pixelswithin the common mask, and determine, for each corresponding characterof the master image, the average value associated with pixels within thecommon mask; determine, for each character of the common mask, a metricbetween the average values associated with the corresponding charactersin the scanned and master images, wherein the metric between the averagevalues comprises a Euclidean distance between the average valuesassociated with the corresponding characters in the scanned and masterimages; determine a mean associated with each determined metric;determine whether a mean determined from each mean associated with eachdetermined metric is greater than a specified mean value threshold; andin response to a determination that the mean determined from each meanassociated with each determined metric is greater than the specifiedmean value threshold, determine a reduction in print quality of theprinted physical medium.
 12. The non-transitory computer readable mediumaccording to claim 11, further comprising machine readable instructions,when executed, further cause the processor to: analyze a histogram ofthe determined metrics for characters of the common mask to diagnose theprint quality of the printed physical medium.
 13. The non-transitorycomputer readable medium according to claim 11, further comprisingmachine readable instructions, when executed, further cause theprocessor to: divide the printed physical medium into a plurality ofsections; generate, for each section of the plurality of sections, ahistogram of determined metrics for characters of the common mask; andanalyze the histogram of the determined metrics for a section of theplurality of sections to diagnose the print quality of the section ofthe plurality of sections of the printed physical medium.
 14. Thenon-transitory computer readable medium according to claim 11, furthercomprising machine readable instructions, when executed, further causethe processor to: determine whether a histogram of the determinedmetrics for characters of the common mask includes a relatively narrowspread or a relatively wide spread relative to a specified spread of thehistogram; and in response to a determination that the histogram of thedetermined metrics includes the relatively wide spread, determine thereduction in the print quality of the printed physical medium.